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Evaluate gradient-boosting model performance

Evaluate gradient-boosting model performance - Python Tutorial

From the course: NLP with Python for Machine Learning Essential Training

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Evaluate gradient-boosting model performance

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- [Instructor] We're starting to notice that model building is an iterative process. Unfortunately, we don't just write a couple lines of code, fit a model, and then wipe our hands of it. We start with an initial exploration, then we take what we learned, and we dive a little bit deeper to learn a little bit more. Then we take what we learned in that phase, and dive even deeper on a more specific model. Until eventually, we've a built a model that we're confident in, and that we thoroughly evaluated. With that context, we're going to enter the next phase of our build using GridSearchCV to explore our model, while allowing our prior exploration to point us in the right direction. So just to recap very quickly. GridSearch means setting up different parameter settings that you want to test, and then exhaustively searching that entire grid to determine the best model. And cross-validation means you take your dataset, divide it into k subsets, and then you repeat the holdout test method…

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